Field
Certain aspects of the present disclosure generally relate to artificial nervous systems and, more particularly, to implementing an artificial nervous system with dynamic attention resource allocation.
Background
An artificial neural network, which may comprise an interconnected group of artificial neurons (i.e., neuron models), is a computational device or represents a method to be performed by a computational device. Artificial neural networks may have corresponding structure and/or function in biological neural networks. However, artificial neural networks may provide innovative and useful computational techniques for certain applications in which traditional computational techniques are cumbersome, impractical, or inadequate. Because artificial neural networks can infer a function from observations, such networks are particularly useful in applications where the complexity of the task or data makes the design of the function by conventional techniques burdensome.
Certain biological functions may be too complex to model in some systems without optimizations. For example, the retina may be too complex to model in real-time or even close to real-time in many systems without optimizations for performance improvement. More generally, artificial nervous systems and peripheral sensor processors have limits in the amount of computation for real-time operations.